Coronary artery disease is the single most common cause of death from cardiovascular disease in the western world. The heart muscle receives its blood supply through the coronary arteries, and atherosclerosis is the most common pathophysiologic process occurring in the coronary arteries giving rise to coronary artery disease (CAD). Atherosclerosis is a process that builds up plaques within the artery, and the blood flow can therefore be reduced or even blocked by the plaque. The constantly working heart requires a continuous and efficient blood supply in order to work properly. Defects in the blood supply may be very severe and even fatal. Increasing degrees of luminal diameter reduction or stenosis of the coronary artery will first limit reserve flow, then reduce flow at rest and may finally totally occlude the vessel.
There is a need for measuring/detecting coronary artery stenosis for clinicians and other medical professionals to diagnose CAD. Once a diagnosis has been made a cure/treatment could be started.
Today several non-invasive techniques for measuring/detecting the severity of a stenosis or its presence inside a coronary artery exist. This can be done by magnetic resonance imaging (MRI), in vivo intravascular ultrasound (IVUS) or optical coherence tomography (OCT). However, the above-mentioned techniques are all rather complicated and expensive to use and therefore only patients with specific symptoms are offered such examinations. The consequence is that most patients have a critical stenosis when examined.
Clinicians and other medical professionals have long relied on auscultatory sounds such as cardiovascular sounds to aid in the detection and diagnosis of physiological conditions. For instance, a clinician may utilize a stethoscope to monitor and record heart sounds in order to detect heart valve diseases. Furthermore, the recorded heart sounds could be digitized, saved and stored as data files for later analysis. Devices have been developed that apply algorithms to electronically recorded auscultatory sounds. One example is an automated blood-pressure monitoring device. Other examples include analysis systems that attempt to automatically detect physiological conditions based on the analysis of auscultatory sounds. For instance, artificial neural networks have been discussed as one possible mechanism for analyzing auscultatory sounds and providing an automated diagnosis or suggested diagnosis. It is difficult to provide an automated device for classifying auscultatory sounds according to coronary stenosis using these conventional techniques, because it is very difficult to adapt these techniques to take account of the differences between persons. Two different persons would influence the auscultatory sounds differently and the auscultatory sounds from two patients could be different even though both persons suffer from coronary stenosis. Moreover, it is often difficult to implement the conventional techniques in a manner that may be applied in real-time or pseudo real-time to aid the clinician.
Many clinicians prefer to use a digital stethoscope to acquire auscultatory sounds because they are familiar with stethoscopes, but the quality of auscultatory sounds acquired with a digital stethoscope is very often poor compared to auscultatory sounds recorded by more advanced systems. The quality of such auscultatory sounds is often reduced because additional noise is introduced during the recording—e.g. due to friction between the microphone and the patient's chest or due to noise in the surroundings. Further, it is a very intimate situation when a clinician records an auscultatory sound using a stethoscope because the distance between the patient and the clinician is very small, and the consequence is that the auscultatory recording is very short. Therefore only a small amount of data suitable for performing an analysis of the coronary artery disease is acquired when using a stethoscope and the analyses performed by known techniques are therefore very often incorrect.
U.S. Pat. No. 5,036,857 discloses a method and a system for non-invasively detecting Coronary Artery Disease. The method comprises analyzing the diastolic heart sounds detected from a patient's chest cavity during the diastolic portion of the heart cycle in order to identify a low level auditory component associated with turbulent blood flow in partially occluded coronary arteries. These diastolic heart sounds are modelled using advanced signal processing techniques such as Autoregressive (AR), Autoregressive Moving Averaging (ARMA) and Eigenvector methods, so that the presence of such an auditory component may be reliably indicated. The system includes an acoustic transducer, a pulse sensor device, signal processor means and a diagnostic display. Additionally, the system includes a controller for automatically sequencing data collection, analysis and display stages, therefore requiring a minimum of operator interaction. This method and system analyzes the amount of noise in the diastolic segments, and diastolic segments with a large amount of noise are discarded and not used in the analysis of low level auditory components associated with turbulent blood flow in partially occluded coronary arteries. Therefore a large amount of diastolic segments need to be recorded in order to achieve a proper analysis, and the sound recording should therefore be very long or repeated many times. This is in many clinical situations not possible especially when using a digital stethoscope to record the heart sound.